Medical image processing apparatus, medical image processing method, medium, and medical image processing system

ABSTRACT

A medical image processing apparatus includes a memory; and at least one processor configured to execute detecting one or more vertebral bodies and one or more intervertebral disks in a medical image; labeling each part satisfying a predetermined condition among the one or more vertebral bodies and the one or more intervertebral disks detected by the detecting; interpolating a vertebral body or an intervertebral disk in a case where the one or more vertebral bodies and the one or more intervertebral disks detected by the detecting do not include the vertebral body or the intervertebral disk that satisfies the predetermined condition; and executing the labeling also for the vertebral body or the intervertebral disk interpolated by the interpolating.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure relates to a medical image processing apparatus,a medical image processing method, a medium, and a medical imageprocessing system.

2. Description of the Related Art

Conventionally, from a medical image obtained by a medical imagediagnostic device such as an X-ray CT device, an MRI device (magneticresonance device), or the like, in order to determine an abnormal partand to align the position, parts of the body such as vertebrae,intervertebral disks, sacrums, and the like are detected to assignlabels to these parts.

For example, a method has been proposed for executing labeling on an MRIimage while estimating the positions of vertebral bodies andintervertebral disks.

However, medical images to be processed may often include unclearvertebral bodies, with which it may be difficult to detect vertebralbodies in such medical images. In particular, in the case where themedical image is an X-ray image, the vertebral bodies tend to beunclear. Also, since an intervertebral disk is not imaged to have a diskform in an X-ray image, in the case where the medical image is an X-rayimage, intervertebral disks cannot be detected. Furthermore, if anintervertebral disk has been compressed to become thinner than a normalone, the intervertebral disk may look unclear even in an MRI image orthe like. The conventional method of executing labeling on the MRI imagehas been targeted at clear images in which parts are clear enough to bedetected, and is not capable of estimating the positions of vertebralbodies and intervertebral disks in the case of the images of thevertebral bodies and intervertebral disks being unclear.

As such, according to the conventional techniques, it has been often thecase that some of the parts included in a medical image cannot bedetected, and hence, labeling cannot be executed appropriately.

SUMMARY OF THE INVENTION

According to an aspect, a medical image processing apparatus includes amemory; and at least one processor configured to execute detecting oneor more vertebral bodies and one or more intervertebral disks in amedical image; labeling each part satisfying a predetermined conditionamong the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting; interpolating avertebral body or an intervertebral disk in a case where the one or morevertebral bodies and the one or more intervertebral disks detected bythe detecting do not include the vertebral body or the intervertebraldisk that satisfies the predetermined condition; and executing thelabeling also for the vertebral body or the intervertebral diskinterpolated by the interpolating.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a medical image processing systemaccording to an example embodiment in the present disclosure;

FIG. 2 is a functional block diagram illustrating a configuration of amedical image processing apparatus;

FIG. 3 is a functional block diagram illustrating details of a labelerand an interpolator;

FIG. 4 is a flowchart illustrating operations of a medical imageprocessing apparatus;

FIG. 5 is a flowchart illustrating a process executed by a labeler andan interpolator;

FIG. 6 is a flowchart illustrating a process executed by a next pointdeterminer and an interpolation point calculator;

FIG. 7A is a diagram (first example) illustrating a method of processinga medical image including a sacrum;

FIG. 7B is a diagram (second example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7C is a diagram (third example) illustrating a method of processinga medical image including a sacrum;

FIG. 7D is a diagram (fourth example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7E is a diagram (fifth example) illustrating a method of processinga medical image including a sacrum;

FIG. 7F is a diagram (sixth example) illustrating a method of processinga medical image including a sacrum;

FIG. 7G is a diagram (seventh example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7H is a diagram (eighth example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7I is a diagram (ninth example) illustrating a method of processinga medical image including a sacrum;

FIG. 7J is a diagram (tenth example) illustrating a method of processinga medical image including a sacrum;

FIG. 7K is a diagram (eleventh example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7L is a diagram (twelfth example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 7M is a diagram (thirteenth example) illustrating a method ofprocessing a medical image including a sacrum;

FIG. 8A is a diagram (first example) illustrating an example of a methodof determining an angle condition;

FIG. 8B is a diagram (second example) illustrating an example of amethod of determining an angle condition;

FIG. 8C is a diagram (third example) illustrating an example of a methodof determining an angle condition;

FIG. 8D is a diagram (fourth example) illustrating an example of amethod of determining an angle condition;

FIG. 8E is a diagram (fifth example) illustrating an example of adetermination method of an angle condition;

FIG. 9 is a flowchart illustrating a labeling process of a lumbarvertebra;

FIG. 10A is a diagram illustrating an example (first example) of adisplay of a result of labeling;

FIG. 10B is a diagram illustrating an example (second example) of adisplay of a result of labeling;

FIG. 10C is a diagram illustrating an example (third example) oflabeling result display;

FIG. 10D is a diagram illustrating an example (fourth example) of adisplay of a result of labeling;

FIG. 10E is a diagram illustrating an example (fifth example) of adisplay of a result of labeling;

FIG. 10F is a diagram illustrating an example (sixth example) of adisplay of a result of labeling;

FIG. 11A is a diagram (first example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11B is a diagram (second example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11C is a diagram (third example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11D is a diagram (fourth example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11E is a diagram (fifth example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11F is a diagram (sixth example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11G is a diagram (seventh example) illustrating a method ofprocessing a medical image including a second cervical vertebra;

FIG. 11H is a diagram (eighth) illustrating a method of processing amedical image including a second cervical vertebra;

FIG. 11I is a diagram (ninth) illustrating a method of processing amedical image including a second cervical vertebra;

FIG. 11J is a diagram (tenth) illustrating a method of processing amedical image including a second cervical vertebra;

FIG. 12 is a flowchart illustrating a labeling process of cervicalvertebrae;

FIG. 13A is a flowchart (first example) illustrating a process oflearning and detection executed by a detector;

FIG. 13B is a flowchart (second example) illustrating a process oflearning and detection executed by a detector;

FIG. 13C is a flowchart (third example) illustrating a process oflearning and detection executed by a detector;

FIG. 13D is a flowchart (fourth) illustrating a process of learning anddetection executed by a detector;

FIG. 13E is a flowchart (fifth example) illustrating a process oflearning and detection executed by a detector;

FIG. 13F is a flowchart (sixth example) illustrating a process oflearning and detection executed by a detector;

FIG. 14A is a diagram illustrating a content of learning with respect tolumbar vertebrae;

FIG. 14B is a diagram illustrating a content of learning with respect tolumbar vertebrae;

FIG. 15A is a diagram illustrating a content of learning with respect tocervical vertebrae; and

FIG. 15B is a diagram illustrating a content of learning with respect tocervical vertebrae.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following, example embodiments in the present disclosure will bedescribed in detail. Note that the present disclosure is not limited tothe following example embodiments at all and can be implemented withappropriate modification within the scope of the object in the presentdisclosure.

According to the disclosed techniques, it is possible to appropriatelyexecute labeling on a medical image even when an unclear part isincluded.

FIG. 1 is a diagram illustrating a medical image processing systemaccording to an example embodiment in the present disclosure. Themedical image processing system 300 includes a medical image processingapparatus 100 and a medical image capturing apparatus 200.

The medical image capturing apparatus 200 captures an X-ray image (X-raypicture) of a subject, and includes, for example, a flat panel detector(FPD). The medical image capturing apparatus 200 may capture an MRI(Magnetic Resonance Imaging) image by MRI. The medical image processingapparatus 100 processes medical image data such as a medical imagecaptured by the medical image capturing apparatus 200.

The medical image processing apparatus 100 includes a CPU (CentralProcessing Unit) 101, a ROM (Read-Only Memory) 102, and a RAM (RandomAccess Memory) 103. The CPU 101, the ROM 102, and the RAM 103 constitutea so-called computer. The medical image processing apparatus 100includes an auxiliary storage 104, a display 105, an input device 106,and an external interface (I/F) 107. These components of the medicalimage processing apparatus 100 are mutually connected via a bus 108.

The CPU 101 executes various programs (e.g., a medical image processingprogram) stored in the auxiliary storage 104.

The ROM 102 is a nonvolatile main memory device. The ROM 102 storesvarious programs, data, and the like necessary for the CPU 101 toexecute various programs stored in the auxiliary storage 104.Specifically, the ROM 102 stores a boot program such as BIOS (BasicInput/Output System) and/or EFI (Extensible Firmware Interface).

The RAM 103 is a volatile main memory device such as a DRAM (DynamicRandom Access Memory) or an SRAM (Static Random Access Memory). The RAM103 functions as a work area on which the various programs stored in theauxiliary storage 104 are loaded when executed by the CPU 101.

The auxiliary storage 104 is an auxiliary storage device to storevarious programs executed by the CPU 101, and various items of datagenerated by the CPU 101 when executing the various programs.

The display 105 displays a medical image to which labeling has beenapplied, or the like. The input device 106 includes a mouse, a keyboard,or both of these, and is used by a doctor or the like to input variouscommands (data selection command, superimposed display command, etc.)into the medical image processing apparatus 100. The external I/F 107includes, for example, a communication device for communicating with themedical image capturing apparatus 200, into which a medical imagecaptured by the medical image capturing apparatus 200 is input. Theexternal I/F 107 may include a slot for an external recording medium,and a medical image may be obtained from an external recording mediumsuch as an IC card, a flexible disk, a CD, a DVD, an SD memory card, aUSB memory, or the like.

Next, a functional configuration of the medical image processingapparatus 100 will be described. As described above, the CPU 101implements each function of the medical image processing apparatus 100by loading programs and data stored in the ROM 102, the auxiliarystorage 104, and the like on the RAM 103 and executing a process. FIG. 2is a functional block diagram illustrating a configuration of themedical image processing apparatus 100. As illustrated in FIG. 2, themedical image processing apparatus 100 includes an obtainer 151, adetector 152, a labeler 153, an interpolator 154, and a displaycontroller 155.

The obtainer 151 obtains medical image data from the outside via theexternal I/F 107.

Based on a result of learning executed in advance, the detector 152detects predetermined parts such as vertebral bodies (including secondcervical vertebrae), intervertebral disks, sacrums, and the like in amedical image obtained by the obtainer 151, to detect detection pointsfor the respective detected parts. The method of learning is not limitedin particular. For example, the detector 152 may generate patches byusing a sliding window for the entirety or a selected region of amedical image, to identify a part of each patch. Also, when generatingpatches, object detection may be executed to identify each extractedregion. A detection result may be classified by using at least twogroups among the vertebral bodies, intervertebral disks, sacrums, andsecond cervical vertebrae; and the other regions.

The labeler 153 executes labeling sacrums, vertebral bodies (includingsecond cervical vertebrae), and intervertebral disks detected by thedetector 152 that satisfy predetermined conditions. For example, in thecase where vertebral bodies, intervertebral disks, and sacrums have beendetected, the labeler 153 sets the detection point of a sacrum as thefirst reference point to executes labeling the sacrum, an intervertebraldisk, a vertebral body, another intervertebral disk, another vertebralbody, and so on in this order.

When it has been determined by the labeler 153 that the vertebral bodies(including the second cervical vertebrae) and the intervertebral disksdetected by the detector 152 include a part not satisfying thepredetermined condition described above, the interpolator 154interpolates such a vertebral body or intervertebral disk. For example,if there is a vertebral body or intervertebral disk in a medical imagethat cannot be detected by the detector 152, the interpolator 154interpolates this part by using, for example, at least two groups amongthe vertebral bodies, intervertebral disks, sacrums, and second cervicalvertebrae that have been detected. The labeler 153 also labels thevertebral body or intervertebral disk interpolated by the interpolator154. The process of the labeler 153 may be executed in parallel with theprocess executed by the interpolator 154.

The display controller 155 causes the display 105 to display data onwhich labeling has been executed by the labeler 153. The data is notlimited to be displayed via the display controller 155, and may beoutput as a file.

Next, the labeler 153 and the interpolator 154 will be described indetail. FIG. 3 is a functional block diagram illustrating the labeler153 and the interpolator 154 in detail. As illustrated in FIG. 3, thelabeler 153 includes an approximate curve generator 161, a referencepoint determiner 162, and a next point determiner 163, and theinterpolator 154 includes an interpolation point calculator 171.

The approximate curve generator 161 generates an approximate curveconstituted with multiple line segments connecting the detection pointsof parts detected by the detector 152.

The reference point determiner 162 determines a point served as areference when executing labeling, and updates the reference point asthe process progresses. For example, if a sacrum is included, the sacrumis set as the first reference point, and the reference point is updatedas the process progresses. For example, if a second cervical vertebra isincluded, the second cervical vertebra is set as the first referencepoint, and the reference point is updated as the process progresses.

Based on the reference point determined by the reference pointdeterminer 162, the next point determiner 163 determines whether avertebral body or an intervertebral disk that satisfies a predeterminedcondition set for the reference point has been detected, and dependingon the determination result, determines a point to be served as the nextreference point (next point).

If it has been determined by the next point determiner 163 that avertebral body or an intervertebral disk satisfying the above-mentionedpredetermined condition has not been detected, the interpolation pointcalculator 171 refers to the above-mentioned predetermined condition tocalculate the interpolation point of the vertebral body orintervertebral disk. The method of calculating an interpolation pointwill be described in detail later.

Next, operations of the medical image processing apparatus 100 will bedescribed. FIG. 4 is a flowchart illustrating operations of the medicalimage processing apparatus 100. These operations are implemented by theCPU 101 executing a program stored in the auxiliary storage 104.

First, the obtainer 151 obtains medical image data via the external I/F107 (Step S101). Next, the detector 152 detects predetermined parts suchas vertebral bodies, intervertebral disks, sacrums, and the like fromthe medical image data obtained by the obtainer 151, to determine adetection point for each of the detected parts (Step S102). Thereafter,the labeler 153 labels the predetermined parts included in the medicalimage data obtained by the obtainer 151 (Step S103). Although theprocess executed by the labeler 153 will be described in detail later,the labeler 153 causes the interpolator 154 to interpolate a vertebralbody or intervertebral disk where necessary, to execute labeling. Afterthe labeling executed by the labeler 153, the display controller 155causes the display 105 to display the labeled medical image. Examples ofthe display on the display 105 will be described later.

Next, a process executed by the labeler 153 and the interpolator 154will be described in detail. FIG. 5 is a flowchart illustrating aprocess executed by the labeler 153 and the interpolator 154. Thisprocess is implemented by the CPU 101 executing a program stored in theauxiliary storage 104.

First, the approximate curve generator 161 generates an approximatecurve of multiple line segments connecting the detection pointsdetermined by the detector 152 (Step S111). Next, the reference pointdeterminer 162 determines the first reference point from among thedetection points determined by the detector 152. For example, if asacrum is included, the detection point of the sacrum is set as thefirst reference point, or if the second cervical vertebra is included,the second cervical vertebra is set as the first reference point (StepS112). Thereafter, the next point determiner 163 interoperates with theinterpolation point calculator 171 when necessary to determine a pointto be set as the next reference point based on the reference pointdetermined by the reference point determiner 162 (Step S113). Theprocess executed the next point determiner 163 and the interpolationpoint calculator 171 will be described in detail later. After adetermination executed by the next point determiner 163, the referencepoint determiner 162 determines whether the determination has beencompleted for all of the predetermined parts (Step S114), and if notcompleted, updates the reference point to a labeling point determined atStep S113 (Step S115), and Step S113 is executed again. Meanwhile, it ispreferable that the approximate curve is formed, for example, to passthrough a detection point set as the first reference point, or to havethis detection point located outside the arc. This is because a tangentpassing through the reference point may be generated later.

Next, the process executed by the next point determiner 163 and theinterpolation point calculator 171 will be described in detail. FIG. 6is a flowchart illustrating the process executed by the next pointdeterminer 163 and the interpolation point calculator 171. This processis implemented by the CPU 101 executing a program stored in theauxiliary storage 104.

First, the next point determiner 163 determines candidates for the nextpoint from among the detection points determined by the detector 152(Step S121). For example, if the current reference point is set on asacrum, a second cervical vertebra, or another vertebral body, thedetection points of intervertebral disks are determined as candidatesfor the next point. Also, if the current reference point is anintervertebral disk, the detected points of vertebral bodies aredetected as candidates of the next point. Next, the next pointdeterminer 163 determines whether there is a detection point thatsatisfies a distance condition predetermined with respect to the currentreference point among the candidates of the next point (Step S122). Ifthere is a detection point that satisfies the distance condition, thenext point determiner 163 calculates an angle condition with respect tothe current reference point (Step S123), to determine whether thedetection point that satisfies the distance condition satisfies thisangle condition (Step S124). If this detection point satisfies the anglecondition, the next point determiner 163 determines this detection pointas the labeling point of a part adjacent to the current reference point(Step S125). On the other hand, if there is no detection point thatsatisfies the distance condition at Step S122 or if the angle conditionis not satisfied at Step S124, the interpolation point calculator 171calculates an interpolation point (Step S126), and the next pointdeterminer 163 determines this interpolation point as the labeling pointof the part adjacent to the current reference point (Step S127). Thenext point determiner 163 sets the labeling point determined in this wayas the next reference point (next point).

Note that in this example, although the executed process is based onboth of the distance condition and the angle condition, the process maybe executed based on only one of the distance condition and the anglecondition.

Here, a specific example of the operations of the medical imageprocessing apparatus 100 will be described. FIGS. 7A to 7M are diagramsillustrating a method of processing a medical image including a sacrum.

In this example, as illustrated in FIG. 7A, assume that a medical image201 is used in which a first lumbar vertebra L1, a second lumbarvertebra L2, a third lumbar vertebra L3, a fourth lumbar vertebra L4, afifth lumbar vertebra L5, and a sacrum S1 are imaged. There is anintervertebral disk DL1 between the first lumbar vertebra L1 and thesecond lumbar vertebra L2; there is an intervertebral disk DL2 betweenthe second lumbar vertebra L2 and the third lumbar vertebra L3; there isan intervertebral disk DL3 between the third lumbar vertebra L3 and thefourth lumbar vertebra L4; there is an intervertebral disk DL4 betweenthe fourth lumbar vertebra L4 and the fifth lumbar vertebra L5; andthere is an intervertebral disk DL5 between the fifth lumbar vertebra L5and the sacrum S1. The data of this medical image 201 is obtained by theobtainer 151.

The detector 152 attempts to detect the first lumbar vertebra L1, thesecond lumbar vertebra L2, the third lumbar vertebra L3, the fourthlumbar vertebra L4, the fifth lumbar vertebra L5, the sacrum S1, theintervertebral disk DL1, the intervertebral disk DL2, the intervertebraldisk DL3, the intervertebral disk DL4, and the intervertebral disk DL5,and then, determines detection points for the parts that have beendetected. In this example, as illustrated in FIG. 7B, assume that adetection point 211 of the sacrum S1 has been determined, detectionpoints 212 have been determined for a part of the intervertebral disks,and detection points 213 have been determined for a part of the lumbarvertebrae; however, detection points could not be detected for anotherpart of the intervertebral disks and another part of the lumbarvertebrae.

As illustrated in FIG. 7C, the approximate curve generator 161 of thelabeler 153 generates an approximate curve 214 of a group of linesegments connecting the detection points 211, 212 and 213 determined bythe detector 152.

As illustrated in FIG. 7D, the reference point determiner 162 of thelabeler 153 determines the first reference point from among thedetection points 211, 212 and 213 determined by the detector 152. Inthis example, the detection point 211 of the sacrum S1 is determined asthe first reference point.

As illustrated in FIG. 7E, since the current reference point is set onthe sacrum S1, the next point determiner 163 of the labeler 153determines multiple detection points 212 of the intervertebral disks ascandidates for the next point.

Then, the next point determiner 163 determines whether there is adetection point that satisfies a predetermined distance condition withrespect to the sacrum S1 among the multiple detection points 212 of theintervertebral disks. For example, as illustrated in FIG. 7F, in acertain direction, for example, a direction from the foot toward thehead (+X direction), the next point determiner 163 determines whetherthere is a detection point whose distance from the detection point 211is less than or equal to an interpolation distance A1 predetermined withrespect to the sacrum S1. Here, assume that the detection point 212 ofthe intervertebral disk DL5 satisfies the distance condition.

Next, the next point determiner 163 calculates an angle condition withrespect to the detection point 211, to determine whether the detectionpoint 212 of the intervertebral disk DL5 satisfies this angle condition.For example, as illustrated in FIG. 7G, the next point determiner 163calculates two straight lines 231 and 232 by using the approximate curve214, to determine whether the relationship between the angle formed bythese straight lines and the position of the detection point 212satisfies the predetermined condition.

Here, a method of determining an angle condition will be described.FIGS. 8A to 8E are diagrams illustrating an example of a method ofdetermining an angle condition.

In this example, as illustrated in FIG. 8A, assume that there are twoparts having a boundary 301 interposed, one of which has a referencepoint 302, and the other has a candidate detection point of the nextpoint. Also, assume that an approximate curve 305 has been obtained andan interpolation distance 303 is set with respect to the reference point302.

As illustrated in FIG. 8B, if the candidate detection point of the nextpoint is the detection point 312 or 313 whose distance from thereference point 302 is less than or equal to the interpolation distance303, the next point determiner 163 calculates a tangent 331 of theapproximation curve 305 that passes through the reference point 302. Thenext point determiner 163 further calculates a tangent 332 of theapproximate curve 305 passing through the intersection between the curve335, which is separated from the reference point 302 by theinterpolation distance 303, and the approximate curve 305, to calculatea straight line 333 that is parallel with the tangent 332 and passesthrough the reference point 302. Then, by arithmetically using themagnitude of the angle, the next point determiner 163 determines whetherthe detection point 312 or 313 is located between the tangent 331 andthe straight line 333. For example, assuming that an approximate curveis represented by Expression (1) where x represents the x coordinate, yrepresents the y coordinate, and pn represents a parameter of theapproximate curve, the tangent passing through the reference point 302is expressed as follows. In other words, representing the x coordinateby x_(s) and the y coordinate by y_(s) of the reference point 302,Equation (2) holds; therefore, the tangent passing through the referencepoint 302 is expressed by Equations (3) to (5).

$\begin{matrix}{x = {{p_{1}y^{3}} + {p_{2}y^{2}} + {p_{3}y} + p_{4}}} & (1) \\{x_{s} = {{p_{1}y_{s}^{3}} + {p_{2}y_{s}^{2}} + {p_{3}y_{s}} + p_{4}}} & (2) \\{y = {{ax} + b}} & (3) \\{a = \frac{1}{{3p_{1}y_{s}^{2}} + {2p_{2}y_{s}} + p_{3}}} & (4) \\{b = {{- {ax}_{s}} + y_{s}}} & (5)\end{matrix}$

If the candidate detection point of the next point is the detectionpoint 312 located between the tangent 331 and the straight line 333, asillustrated in FIG. 8C, the next point determiner 163 determines thedetection point 312 as it is as the labeling point. On the other hand,if the candidate detection point of the next point is the detectionpoint 313 outside the area between the tangent 331 and the straight line333, as illustrated in FIG. 8D, the interpolation point calculator 171determines the intersection point between the tangent 331 and the curve335 as an interpolation point 323, and the next point determiner 163determines the interpolation point 323 as the labeling point. Forexample, assuming that the y coordinate of the reference point 302 isy_(s), the x coordinate is x_(s), and the curve 335 that is separatedfrom the reference point 302 by the distance D is a circle, Equation (6)holds. Therefore, by solving simultaneous equations with Equation (3) ofthe tangent passing through the reference point 302, candidates of the ycoordinate y_(d) and the x coordinate x_(d) of the interpolation point323 can be calculated as represented by Equations (7) and (8).

$\begin{matrix}{{\left( {x - x_{s}} \right)^{2} + \left( {y - y_{s}} \right)^{2}} = D^{2}} & (6) \\{y_{d} = {{a \cdot x_{d}} + b}} & (7) \\{x_{d} = {{- \frac{{ab} - {ay}_{s} - x_{s}}{a^{2} + 1}} \pm \sqrt{{abs}\left( {\frac{D^{2} - x_{s}^{2} - \left( {b - y_{s}} \right)^{2}}{a^{2} + 1} + \left( \frac{{ab} - {ay}_{s} - x_{s}}{a^{2} + 1} \right)^{2}} \right)}}} & (8)\end{matrix}$

Then, from among combinations of the y coordinate y_(d) and the xcoordinate x_(d), a combination having the smallest y coordinate can beobtained as the interpolation point 323.

If the candidate detection point of the next point is the detectionpoint 311 whose distance from the reference point 302 is greater thanthe interpolation distance 303, as illustrated in FIG. 8E, the nextpoint determiner 163 calculates the tangent 331 of the approximate curve305 passing through the reference point 302. Then, the interpolationpoint calculator 171 determines the intersection point between thetangent 331 and the curve 335 as an interpolation point 321, and thenext point determiner 163 determines the interpolation point 321 as thelabeling point.

Note that as for the interpolation distance, information on a detectionframe of the reference point may be used. The information on a detectionframe may include, for example, information on the sizes of a vertebralbody, an intervertebral disk, and a sacrum. For example, if thedetection frame of a lumbar vertebra as the reference point is used, thesize of the lumbar vertebra can be known, and hence, the boundarybetween the lumbar vertebra and the intervertebral disk can be obtained.Since the intervertebral disk has the size of approximately ½ to ⅓ timesthe size of the lumbar vertebra, the size of ½ to ⅓ times the detectionframe of the lumbar vertebra can be estimated as the size of theintervertebral disk, namely, the interpolation distance. Therefore, ifthe detection frame of the lumbar vertebrae is known, the boundarybetween the lumbar vertebra and the intervertebral disk can be located,and the interpolation distance can also be estimated, and thereby, theposition of the intervertebral disk as the next point can be estimated.Also, the straight line 333 may be calculated by using the informationon the detection frame. Also, a distance centered on the reference pointmay be used as the interpolation distance.

In this way, it is possible to determine whether the angle condition issatisfied. In the example illustrated in FIG. 7G, assume that thedetection point 212 of the intervertebral disk DL5 satisfies the anglecondition. In this case, as illustrated in FIG. 7H, the next pointdeterminer 163 sets the detection point 212 of the intervertebral diskDL5 as it is as the labeling point of the intervertebral disk DL5, toset the labeling point as the next reference point.

Next, the next point determiner 163 executes a similar process with thedetection point 212 of the intervertebral disk DL5 as the referencepoint. Then in this example, as illustrated in FIG. 7I, assume that thedetection point 213 of the fifth lumbar vertebra L5 as it is is set asthe labeling point of the fifth lumbar vertebra L5.

Next, the next point determiner 163 executes a similar process with thedetection point 213 of the fifth lumbar vertebra L5 as the referencepoint. Then, in this example, as illustrated in FIG. 7J, assume that theintervertebral disk DL4 between the fourth lumbar vertebra L4 and thefifth lumbar vertebra L5 has not been detected. In this case, there isno detection point whose distance in the +X direction is less than orequal to the interpolation distance A3, which has been set for thereference point of the fifth lumbar vertebra L5. Therefore, asillustrated in FIG. 7K, the interpolation point calculator 171calculates an interpolation point 222 of the intervertebral disk DL4.Then, as illustrated in FIG. 7L, the next point determiner 163 sets theinterpolation point 222 of the intervertebral disk DL4 as the labelingpoint of the intervertebral disk DL4, and sets it as the next referencepoint.

By repeating such a process, as illustrated in FIG. 7M, labeling isexecuted for all of the parts. Note that in the example illustrated inFIG. 7M, assume that the first lumbar vertebra L1 and the third lumbarvertebra L3 are not detected, for which interpolation points 223 areset; the intervertebral disk DL4 is not detected, for which theinterpolation point 222 is set; and although the intervertebral disk DL2has been detected, but the detection point does not satisfy the anglecondition, and hence, an interpolation point 222 is set.

Such a labeling process of the lumbar vertebrae (Step S103) can beillustrated as a flowchart in FIG. 9. In other words, after generationof an approximate curve (Step S111), the detection point of a sacrum isdetermined as the first reference point (Step S201). Next, it isdetermined whether the distance condition and/or the angle conditionwith respect to the next point of the reference point (the detectionpoint of the intervertebral disk) are satisfied (Step S202), and ifeither of the conditions is not satisfied, an interpolation point iscalculated (Step S203). Then, the next point (the detection point of theintervertebral disk) or the interpolation point of the current referencepoint is determined as the labeling point, and the reference point isupdated with this determined labeling point of the intervertebral disk(Step S204). Thereafter, it is determined whether the distance conditionand/or the angle condition with respect to the next point of the currentreference point (the detection point of the vertebral body) aresatisfied (Step S205), and if either of the conditions is not satisfied,an interpolation point is calculated (Step S206). Then, the next pointof the current reference point (the detection point of the vertebralbody) or the interpolation point is determined as the labeling point(Step S207). Next, it is determined whether the labeling point of thefirst lumbar vertebra L1 has been determined (Step S208), and if notdetermined, the reference point is updated with the labeling point ofthe vertebral body determined most recently (Step S209), and the processreturns to Step S202.

Next, examples of displays of labeling results will be described. FIGS.10A to 10F are diagrams illustrating examples of displays of labelingresults.

In the example illustrated in FIG. 10A, on a screen 400 of the display105, the labeling point of a sacrum, the labeling points of lumbarvertebrae, and the labeling points of intervertebral disks are displayedin colors different from each other.

In the example illustrated in FIG. 10B, on the screen 400 of the display105, the labeling point of the sacrum, the labeling points of the lumbarvertebrae, and the labeling points of the intervertebral disks aredisplayed in shapes different from each other.

In the example illustrated in FIG. 10C, on the screen 400 of the display105, the labeling point of the sacrum, the labeling points of the lumbarvertebrae, and the labeling points of the intervertebral disks aredisplayed in sizes different from each other.

In the example illustrated in FIG. 10D, on the screen 400 of the display105, the labeling point of the sacrum, the labeling points of the lumbarvertebrae, and the labeling points of the intervertebral disks aredisplayed in colors different from each other, and the interpolationpoints are displayed with blinking.

In the example illustrated in FIG. 10E, on the screen 400 of the display105, the labeling point of the sacrum, the labeling points of the lumbarvertebrae, and the labeling points of the intervertebral disks aredisplayed in colors different from each other, and further, theinterpolation points are displayed in a different color, for example, inblack.

In the example illustrated in FIG. 10F, on the screen 400 of the display105, the labeling point of the sacrum, the labeling points of the lumbarvertebrae, and the labeling points of the intervertebral disks aredisplayed in shapes different from each other, and further, a colordepending on the reliability is attached to each labeling point.

In this way, the display form of a labeling result is not limited, whichcan be changed in terms of the color, shape, size, blinking, etc. ofeach mark. Display control is executed by the display controller 155.

Next, another specific example of operations of the medical imageprocessing apparatus 100 will be described. FIGS. 11A to 11J arediagrams illustrating methods of processing a medical image including asecond cervical vertebra.

In this example, as illustrated in FIG. 11A, assume that a medical image501 is used in which a second cervical vertebra C2, a third cervicalvertebra C3, a fourth cervical vertebra C4, a fifth cervical vertebraC5, a sixth cervical vertebra C6, and a seventh cervical vertebra C7 areimaged. There is an intervertebral disk DC2 between the second cervicalvertebra C2 and the third cervical vertebra C3; there is anintervertebral disk DC3 between the third cervical vertebra C3 and thefourth cervical vertebrate C4; there is an intervertebral disk DC4between the fourth cervical vertebra C4 and the fifth cervical vertebraC5; there is an intervertebral disk DC5 between the fifth cervicalvertebra C5 and the sixth cervical vertebra C6; there is anintervertebral disk DC6 between the sixth cervical vertebra C6 and theseventh cervical vertebra C7; and there is an intervertebral disk DC7under the seventh cervical vertebra C7. The data of the medical image501 is obtained by the obtainer 151.

The detector 152 attempts to detect the second cervical vertebra C2, thethird cervical vertebra C3, the fourth cervical vertebra C4, the fifthcervical vertebra C5, the sixth cervical vertebra C6, the seventhcervical vertebra C7, the intervertebral disk DC2, the intervertebraldisk DC3, the intervertebral disk DC4, the intervertebral disk DC5, theintervertebral disk DC6, and the intervertebral disk DC7, and then,determines the detection points for the parts that have been detected.In this example, as illustrated in FIG. 11B, assume that a detectionpoint 511 of the second cervical vertebra C2 has been determined,detection points 512 have been determined for a part of theintervertebral disks, and detection points 513 have determined for apart of the cervical vertebrae; however, detection points could not bedetected for another part of the intervertebral disks and another partof the cervical vertebrae.

As illustrated in FIG. 11C, the approximate curve generator 161 of thelabeler 153 generates an approximate curve 514 of a group of linesegments connecting the detection points 511, 512 and 513 determined bythe detector 152.

As illustrated in FIG. 11D, the reference point determiner 162 of thelabeler 153 determines a first reference point from among the detectionpoints 511, 512, and 513 determined by the detector 152. In thisexample, the detection point 511 of the second cervical vertebra C2 isdetermined as the first reference point.

As illustrated in FIG. 11E, since the current reference point is thesecond cervical vertebra C2, the next point determiner 163 of thelabeler 153 determines multiple detection points 512 of theintervertebral disks as candidates for the next point.

Then, the next point determiner 163 determines whether there is adetection point that satisfies a predetermined distance condition withrespect to the second cervical vertebra C2 among the multiple detectionpoints 512 of the intervertebral disks. For example, as illustrated inFIG. 11F, in a certain direction (−X direction), the next pointdeterminer 163 determines whether there is a detection point whosedistance from the detection point 511 is less than or equal to aninterpolation distance B1 predetermined with respect to the secondcervical vertebra C2. Here, assume that the intervertebral disk DC2 isnot detected. In this case, there is no detection point whose distancein the −X direction is less than or equal to the interpolation distanceB1. Therefore, as illustrated in FIG. 11G, the interpolation pointcalculator 171 calculates an interpolation point 522 of theintervertebral disk DC2. Then, as illustrated in FIG. 11H, the nextpoint determiner 163 sets the interpolation point 522 of theintervertebral disk DC2 as the labeling point of the intervertebral diskDC2, and sets it as the next reference point.

Next, the next point determiner 163 executes a similar process with theinterpolation point 522 of the intervertebral disk DC2 as the referencepoint. Then, in this example, as illustrated in FIG. 11I, assume thatthe detection point 513 of the third cervical vertebra C3 as it is isset as the labeling point of the third cervical vertebra C3.

By repeating such a process, as illustrated in FIG. 11J, labeling isexecuted for all of the parts. Note that in the example illustrated inFIG. 11J, assume that the fifth cervical vertebra C5 is not detected, towhich the interpolation point 523 is set, and the intervertebral diskDC2 and the intervertebral disk DC6 are not detected, to which theinterpolation points 522 are set.

Such a labeling process of the cervical vertebrae (Step S103) can beillustrated as a flowchart in FIG. 12. In other words, after generationof an approximate curve (Step S111), the detection point of the secondcervical vertebra is determined as the first reference point (StepS301). Next, it is determined whether the distance condition and/or theangle condition with respect to the next point of the reference point(the detection point of the intervertebral disk) are satisfied (StepS202), and if either of the conditions is not satisfied, aninterpolation point is calculated S203). Then, the next point of thecurrent reference point (the detection point of the intervertebral disk)or the interpolation point is determined as the labeling point, and thereference point is updated with this determined labeling point of theintervertebral disk (Step S204). Thereafter, it is determined whetherthe distance condition and/or angle condition with respect to the nextpoint of the current reference point (the detection point of thevertebral body) is satisfied (Step S205), and if either of theconditions is not satisfied, an interpolation point is calculated (StepS206). Then, the next point of the current reference point (thedetection point of the vertebral body) or the interpolation point isdetermined as the labeling point (Step S207). Next, it is determinedwhether the labeling point of the seventh cervical vertebra C7 has beendetermined (Step S208), and if not determined, the reference point isupdated with the labeling point of the vertebral body determined mostrecently (Step S209), and the process returns to Step S202. If thelabeling point of the seventh cervical vertebra C7 has been determined,labeling may be executed for the intervertebral disk DC7.

Here, examples of methods of learning for causing the detector 152 todetect respective parts will be described. FIGS. 13A to 13F areflowcharts illustrating processes of learning and detection executed bythe detector 152.

First, parts to be detected are cut out from a medical image, togenerate training data (image) (Steps S11, S21, S31, S41, S51, and S61).As the training data (image), for example, an image is used in which twoor more of the vertebral bodies, intervertebral disks, sacrums, andsecond cervical vertebrae are included, and other parts are included. Inthe example illustrated in FIG. 13A, the training data (image) includesvertebral bodies, intervertebral disks, sacrums, and other parts. In theexample illustrated in FIG. 13B, the training data (image) includesvertebral bodies, sacrums, and other parts. In the example illustratedin FIG. 13C, the training data (image) includes intervertebral disks,sacrums, and other parts. In the example illustrated in FIG. 13D, thetraining data (image) includes vertebral bodies, intervertebral disks,second cervical vertebrae, and other parts. In the example illustratedin FIG. 13E, the training data (image) includes vertebral bodies, secondcervical vertebrae, and other parts. In the example illustrated in FIG.13F, the training data (image) includes intervertebral disks, secondcervical vertebrae, and other parts.

Also, an image obtained by applying a preprocess to generated trainingdata (image) may be used as the training data (image). For example, dataaugmentation may be applied in order to increase the number of items oftraining data (images) and to set them as the training data (images), inwhich an image is rotated or reversed; the resolution or size of animage is changed; the degree of blur is changed; the amount of noise ischanged; and/or the like. As for augmentation by rotation, with anotation of representing (angle in the clockwise direction: angle in thecounterclockwise direction: increment of rotation angle), theaugmentation may be executed for an image of vertebral bodies by (30degrees: 60 degrees: 5 degrees); for an image of intervertebral disks by(40 degrees: 60 degrees: 10 degrees); for an image of sacrums by (30degrees: 60 degrees: 10 degrees); for an image of parts other than theabove three parts by (5 degrees: 5 degrees: 1 degree); and the like. Asfor augmentation in the size, the augmentation may be executed to setthe vertical width of the rectangular area to be cut=the vertical widthof the cut rectangular area×(1, 1.1, 1.2); the horizontal width of therectangular area to be cut=the horizontal width of the cut rectangulararea×1, 1.1, 1.2, 1.3); and the like. If image processing is applied totraining data (image), processes such as contrast flattening and/or edgeenhancement may be applied.

Next, machine learning is executed (Steps S12, S22, S32, S42, S52, andS62). In the machine learning, labels are assigned to two or more of thevertebral bodies, the intervertebral disks, the sacrums, and the secondcervical vertebrae; and other parts included in the generated trainingdata (image), to generate a detector. This detector can be used as thedetector 152. Also, the number of items of training data (images) to belearned may be changed for each part. For example, in a batch oflearning, a method may be adopted to set the ratios of (vertebralbodies):(intervertebral disks):(sacrums):(other than vertebral bodies,intervertebral disks, and sacrums)=0.30:0.30:0.10:0.30.

Then, when detecting the parts from the medical image (Steps S13, S23,S33, S43, S53, and S63), the detector is applied to a medical imageobtained by the obtainer 151, to treat labels corresponding to theoutput parts or regions as the detection result. In this way, treatinglabels corresponding to the parts as the detection result enables todetect multiple parts at one time, and thereby, the process can beexecuted at a higher speed. Also, detectors may be prepared for therespective parts, which may be, for example, a vertebral body detector,an intervertebral disk detector, a sacrum detector, and a secondcervical vertebra detector, so as to execute learning and detection foreach of the parts. Processing in this way enables to treat each of thedetectors as an independent module, and thereby, enables to select adetector(s) appropriate for the image to be processed, and to make theprocess more flexible. Also, raster scanning that cuts out an image byscanning may be used to provide an image to be input into a detector.The size of a frame of a detection window used for raster scanning maybe set to be compatible with the size of the vertebral body,intervertebral disk, or the sacrum; or may be set by a method in which areference size is set to be changed as a detection window.

Learning of the detector 152 may use CNN (Convolutional Neural Network),SVM (Support Vector Machine), Adaboost, or random forest; or may useActive Shape Model, General Hough Transfer, or Template Matching. Thesecontents are described in, for example, “Vertebra identification usingtemplate matching model and K-means clustering”, “Fully AutomaticVertebra Detection in X-Ray Images Based on Multi-Class SVM”, and “Fastscale-invariant lateral lumbar vertebrae detection and segmentation inX-ray images”. Also, false detection removal or isolation point removalusing RANSAC (Random sample consensus) may be executed for a detectedvertebral body, intervertebral disk, sacrum, or second cervicalvertebra. Further, clustering may be executed for multiple detectedvertebral bodies, intervertebral disks, sacrums, or second cervicalvertebrae, to group the detection points. Also, a cluster obtained afterthe grouping may be treated as a detection point. FIGS. 14A-14B arediagrams illustrating learned contents of learning of lumbar vertebrae;and FIGS. 15A-15B are diagrams illustrating learned contents of cervicalvertebrae. As illustrated in FIG. 14A, clustering may be executed whendetection points around the lumbar vertebrae have been detected for eachof multiple training data items (images), and as illustrated in FIG.14B, each cluster obtained after the grouping may be treated as adetection point around the lumbar vertebrae. Also, as illustrated inFIG. 15A, clustering may be executed when detection points around thecervical vertebrae have been detected for each of multiple training dataitems (images), and as illustrated in FIG. 15B, each cluster obtainedafter the grouping may be treated as detection points around thecervical vertebrae. Also, as a method of false detection removal usingRANSAC, there is a method in which a third-order approximation curve isestimated in consideration of the curvature of the spine, to calculate adistance with the approximate curve as the center, and to remove adetection point whose distance is greater than the distance of half thewidth of a vertebral body, as a false detection.

According to the embodiments, irrespective of the type of a medicalimage, it is possible to appropriately label vertebral bodies andintervertebral disks even when the medical image contains unclearvertebral bodies or intervertebral disks. Furthermore, wheninterpolating an intervertebral disk, not only detection points of theintervertebral disks, but also detection points of the vertebral bodiesare used in the medical image. Therefore, compared with the case whereinterpolation is executed only by using the detection points of theintervertebral disks, the interval between the detection pointsincluding the intervertebral disks and the vertebral bodies becomes halfto be dense, and thereby, the estimation error of an interpolation pointcan be reduced. Furthermore, even if the position of an intervertebraldisk deviates from the normal position as in the case of a slipped disk,it is possible to interpolate an intervertebral disk with high accuracyfrom the detection points of vertebral bodies.

Note that even if a medical image does not include a sacrum and a secondcervical vertebra, at least one vertebral body or intervertebral disk inthe medical image has been known as a specific part of the spine, thedetection point may be set as the first reference point to executelabeling. Therefore, for example, it is also possible to executelabeling on a medical image of thoracic vertebrae, which does notinclude both a sacrum and a second cervical vertebra. Also, it is notnecessary for a medical image of cervical vertebrae to include allcervical vertebrae and intervertebral disks; for example, the image maynot include a sixth cervical vertebra C6 and a seventh cervical vertebraC7 near the shoulders. Similarly, it is not necessary for a medicalimage of lumbar vertebrae to include all lumbar vertebrae andintervertebral disks; for example, the image may not include a firstlumbar vertebra L1 and a second lumbar vertebra L2 near the ribs. Themedical image does not need to be an X-ray image, and may be an MRIimage.

The method of interpolation executed by the interpolator 154 is notlimited in particular. For example, interpolation may be executed byextrapolation or by interpolation. The interpolation point calculator171 may use a positional relationship between two parts detected by thedetector 152, for example, a positional relationship between a vertebralbody and an intervertebral disk, a positional relationship between asacrum and a vertebral body or an intervertebral disk, a positionalrelationship between a second cervical vertebra and another vertebralbody or an intervertebral disk, to interpolate the position of thevertebral body or intervertebral disk to be interpolated. The positionalrelationship here includes a relationship between a distance between thetwo parts or a relationship of an angle formed with any straight line orplane as a reference.

PRIOR ART DOCUMENTS Patent Documents

-   [Patent Document 1] Japanese Unexamined Patent Publication No.    2016-168166-   [Patent Document 2] Japanese Patent No. 6218569

The present application claims priority under 35 U.S.C. § 119 ofJapanese Patent Application No. 2018-015941 filed on Jan. 31, 2018, andJapanese Patent Application No. 2018-111788 filed on Jun. 12, 2018, theentire contents of which are hereby incorporated by reference.

What is claimed is:
 1. A medical image processing apparatus, comprising:a memory; and at least one processor configured to execute detecting oneor more vertebral bodies and one or more intervertebral disks in asagittal sectional view of a medical image; labeling each partsatisfying a predetermined condition among the one or more vertebralbodies and the one or more intervertebral disks detected by thedetecting; interpolating a vertebral body or an intervertebral disk inthe sagittal sectional view of the medical image in a case where the oneor more vertebral bodies and the one or more intervertebral disksdetected by the detecting do not include the vertebral body or theintervertebral disk that satisfies the predetermined condition; andexecuting the labeling also for the vertebral body or the intervertebraldisk interpolated by the interpolating, wherein the interpolatingincludes calculating a position of the vertebral body or theintervertebral disk to be interpolated as an interpolation point, byusing a position of at least one of the one or more vertebral bodies andthe one or more intervertebral disks detected by the detecting.
 2. Themedical image processing apparatus as claimed in claim 1, wherein thedetecting detects a sacrum or a second cervical vertebra in the medicalimage, and wherein the labeling determines whether or not thepredetermined condition is satisfied with the sacrum or the secondcervical vertebra as a reference point.
 3. The medical image processingapparatus as claimed in claim 2, wherein the interpolating includescalculating a position of the vertebral body or the intervertebral diskto be interpolated as an interpolation point, by using a position of atleast one of the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting.
 4. The medical imageprocessing apparatus as claimed in claim 1, wherein the calculatinginterpolates the position of the vertebral body or the intervertebraldisk to be interpolated, by using a positional relationship between twoparts among the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting.
 5. The medical imageprocessing apparatus as claimed in claim 3, wherein the calculatinginterpolates the position of the vertebral body or the intervertebraldisk to be interpolated, by using a positional relationship between twoparts among the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting.
 6. The medical imageprocessing apparatus as claimed in claim 1, further comprising: adisplay device configured to display a result of the labeling executedby said at least one processor.
 7. The medical image processingapparatus as claimed in claim 2, further comprising: a display deviceconfigured to display a result of the labeling executed by said at leastone processor.
 8. The medical image processing apparatus as claimed inclaim 1, further comprising: a display device configured to display aresult of the labeling executed by said at least one processor.
 9. Themedical image processing apparatus as claimed in claim 3, furthercomprising: a display device configured to display a result of thelabeling executed by said at least one processor.
 10. The medical imageprocessing apparatus as claimed in claim 4, further comprising: adisplay device configured to display a result of the labeling executedby said at least one processor.
 11. The medical image processingapparatus as claimed in claim 5, further comprising: a display deviceconfigured to display a result of the labeling executed by said at leastone processor.
 12. A medical image processing method executed by acomputer, the method comprising: detecting one or more vertebral bodiesand one or more intervertebral disks in a sagittal sectional view of amedical image; labeling each part satisfying a predetermined conditionamong the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting; interpolating avertebral body or an intervertebral disk in the sagittal sectional viewof the medical image in a case where the one or more vertebral bodiesand the one or more intervertebral disks detected by the detecting donot include the vertebral body or the intervertebral disk that satisfiesthe predetermined condition; and executing the labeling also for thevertebral body or the intervertebral disk interpolated by theinterpolating, wherein the interpolating includes calculating a positionof the vertebral body or the intervertebral disk to be interpolated asan interpolation point, by using a position of at least one of the oneor more vertebral bodies and the one or more intervertebral disksdetected by the detecting.
 13. The method as claimed in claim 12,wherein the detecting detects a sacrum or a second cervical vertebra inthe medical image, and wherein the labeling determines whether or notthe predetermined condition is satisfied with the sacrum or the secondcervical vertebra as a reference point.
 14. A non-transitorycomputer-readable recording medium having computer readable instructionsstored thereon, which when executed, cause a computer to execute amethod comprising: detecting one or more vertebral bodies and one ormore intervertebral disks in a sagittal sectional view of a medicalimage; labeling each part satisfying a predetermined condition among theone or more vertebral bodies and the one or more intervertebral disksdetected by the detecting; interpolating, a vertebral body or anintervertebral disk in the sagittal sectional view of the medical imagein a case where the one or more vertebral bodies and the one or moreintervertebral disks detected by the detecting do not include thevertebral body or the intervertebral disk that satisfies thepredetermined condition; and executing the labeling also for thevertebral body or the intervertebral disk interpolated by theinterpolating, wherein the interpolating includes calculating a positionof the vertebral body or the intervertebral disk to be interpolated asan interpolation point, by using a position of at least one of the oneor more vertebral bodies and the one or more intervertebral disksdetected by the detecting.
 15. The non-transitory computer-readablerecording medium as claimed in claim 14, wherein the detecting detects asacrum or a second cervical vertebra in the medical image, and whereinthe labeling determines whether or not the predetermined condition issatisfied with the sacrum or the second cervical vertebra as a referencepoint.
 16. A medical image processing system comprising: a medical imagecapturing apparatus configured to capture a medical image; and themedical image processing apparatus as claimed in claim 1 configured toprocess the medical image.
 17. A medical image processing systemcomprising: a medical image capturing apparatus configured to capture amedical image; and the medical image processing apparatus as claimed inclaim 2 configured to process the medical image.
 18. A medical imageprocessing system comprising: a medical image capturing apparatusconfigured to capture a medical image; and the medical image processingapparatus as claimed in claim 1 configured to process the medical image.19. A medical image processing system comprising: a medical imagecapturing apparatus configured to capture a medical image; and themedical image processing apparatus as claimed in claim 3 configured toprocess the medical image.